Workflows

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Galaxy Workflow Documentation: MS Finder Pipeline

This document outlines a MSFinder Galaxy workflow designed for peak annotation. The workflow consists of several steps aimed at preprocessing MS data, filtering, enhancing, and running MSFinder.

Step 1: Data Collection and Preprocessing

Collect if the inchi and smiles are missing from the dataset, and subsequently filter out the spectra which are missing inchi and smiles.

1.1 MSMetaEnhancer: Collect InChi, Isomeric_smiles, and Nominal_mass

...

Type: Galaxy

Creators: Zargham Ahmad, Helge Hecht, Elliott J. Price, Research Infrastructure RECETOX RI (No LM2018121) financed by the Ministry of Education, Youth and Sports, and Operational Programme Research, Development and Innovation - project CETOCOEN EXCELLENCE (No CZ.02.1.01/0.0/0.0/17_043/0009632).

Submitters: Helge Hecht, Zargham Ahmad

DOI: 10.48546/workflowhub.workflow.888.2

Importing single-end multiplexed data (not demultiplexed yet)

Type: Galaxy

Creators: Debjyoti Ghosh, Helmholtz-Zentrum für Umweltforschung - UFZ

Submitter: WorkflowHub Bot

Use DADA2 for sequence quality control. DADA2 is a pipeline for detecting and correcting (where possible) Illumina amplicon sequence data. As implemented in the q2-dada2 plugin, this quality control process will additionally filter any phiX reads (commonly present in marker gene Illumina sequence data) that are identified in the sequencing data, and will filter chimeric sequences.

Type: Galaxy

Creators: Debjyoti Ghosh, Helmholtz-Zentrum für Umweltforschung - UFZ

Submitter: WorkflowHub Bot

Stable

From the R1 and R2 fastq files of a single samples, make a scRNAseq counts matrix, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData Depreciated: use individual workflows insead for multiple samples

Type: Galaxy

Creators: Sarah Williams, Mike Thang, Valentine Murigneaux

Submitter: Sarah Williams

Stable

Takes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.

Type: Galaxy

Creators: Sarah Williams, Mike Thang, Valentine Murigneaux

Submitter: Sarah Williams

Stable

Take a scRNAseq counts matrix from a single sample, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData object.

Depreciated: use individual workflows insead for multiple samples

Type: Galaxy

Creators: Sarah Williams, Mike Thang, Valentine Murigneaux

Submitter: Sarah Williams

Stable

From the R1 and R2 fastq files of a single samples, make a scRNAseq counts matrix, and perform basic QC with scanpy. Then, do further processing by making a UMAP and clustering. Produces a processed AnnData

Depreciated: use individual workflows insead for multiple samples

Type: Galaxy

Creators: Sarah Williams, Mike Thang, Valentine Murigneaux

Submitter: Sarah Williams

Stable

Basic processing of a QC-filtered Anndata Object. UMAP, clustering e.t.c

Type: Galaxy

Creators: Sarah Williams, Mike Thang, Valentine Murigneaux

Submitter: Sarah Williams

Stable

Take an anndata file, and perform basic QC with scanpy. Produces a filtered AnnData object.

Type: Galaxy

Creators: Sarah Williams, Mike Thang, Valentine Murigneaux

Submitter: Sarah Williams

Stable

Takes fastqs and reference data, to produce a single cell counts matrix into and save in annData format - adding a column called sample with the sample name.

Type: Galaxy

Creators: Sarah Williams, Mike Thang, Valentine Murigneaux

Submitter: Sarah Williams

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